Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data
Data analysis and processing allow for acquiring competitive advantages both in the business and academic and research worlds. One of the sciences that carries out this analysis is machine learning, which has evolved with greater emphasis in recent years due to its advantages and applicability in di...
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| Format: | ARTÍCULO DE CONFERENCIA |
| Language: | es_ES |
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Springer Science and Business Media Deutschland GmbH
2024
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| Subjects: | |
| Online Access: | http://dspace.ucuenca.edu.ec/handle/123456789/44208 https://www.scopus.com/record/display.uri?eid=2-s2.0-85174680864&doi=10.1007%2f978-981-99-3091-3_69&origin=inward&txGid=084b8bc02db6d01de6ef029d5e1eccbf |
| _version_ | 1793400625171529728 |
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| author | Flores Siguenza, Pablo Andres Siguenza Guzman, Lorena Catalina |
| author2 | Siguenza Guzman, Lorena Catalina |
| author_facet | Siguenza Guzman, Lorena Catalina Flores Siguenza, Pablo Andres Siguenza Guzman, Lorena Catalina |
| author_sort | Flores Siguenza, Pablo Andres |
| collection | DSpace |
| description | Data analysis and processing allow for acquiring competitive advantages both in the business and academic and research worlds. One of the sciences that carries out this analysis is machine learning, which has evolved with greater emphasis in recent years due to its advantages and applicability in different areas. Aware of the importance and current relevance of data management for industries, especially in the banking sector, this study applies supervised learning techniques to generate classification and prediction models by treating a set of data from an Ecuadorian financial institution. Different algorithms are compared, and each of the steps to follow in constructing the models is explained in detail. This allows the financial entity to classify its clients as VIPs or not with greater certainty, as well as to predict the investment amounts of the potential clients based on variables such as age, occupation, and among others. The main results show that the K-nearest neighbor algorithm with k = 5 is optimal for classification, while for prediction, the multilayer perceptron algorithm is the most favorable. |
| format | ARTÍCULO DE CONFERENCIA |
| id | oai:dspace.ucuenca.edu.ec:123456789-44208 |
| institution | Universidad de Cuenca |
| language | es_ES |
| publishDate | 2024 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | oai:dspace.ucuenca.edu.ec:123456789-442082024-03-11T13:14:29Z Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data Flores Siguenza, Pablo Andres Siguenza Guzman, Lorena Catalina Siguenza Guzman, Lorena Catalina Data analysis Prediction model Machine learning Financial industry Classification model Data analysis and processing allow for acquiring competitive advantages both in the business and academic and research worlds. One of the sciences that carries out this analysis is machine learning, which has evolved with greater emphasis in recent years due to its advantages and applicability in different areas. Aware of the importance and current relevance of data management for industries, especially in the banking sector, this study applies supervised learning techniques to generate classification and prediction models by treating a set of data from an Ecuadorian financial institution. Different algorithms are compared, and each of the steps to follow in constructing the models is explained in detail. This allows the financial entity to classify its clients as VIPs or not with greater certainty, as well as to predict the investment amounts of the potential clients based on variables such as age, occupation, and among others. The main results show that the K-nearest neighbor algorithm with k = 5 is optimal for classification, while for prediction, the multilayer perceptron algorithm is the most favorable. Londes 2024-03-11T13:14:24Z 2024-03-11T13:14:24Z 2023 ARTÍCULO DE CONFERENCIA 978-981993090-6 2367-3370 http://dspace.ucuenca.edu.ec/handle/123456789/44208 https://www.scopus.com/record/display.uri?eid=2-s2.0-85174680864&doi=10.1007%2f978-981-99-3091-3_69&origin=inward&txGid=084b8bc02db6d01de6ef029d5e1eccbf 10.1007/978-981-99-3091-3_69 es_ES application/pdf Springer Science and Business Media Deutschland GmbH Lecture Notes in Networks and Systems |
| spellingShingle | Data analysis Prediction model Machine learning Financial industry Classification model Flores Siguenza, Pablo Andres Siguenza Guzman, Lorena Catalina Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data |
| title | Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data |
| title_full | Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data |
| title_fullStr | Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data |
| title_full_unstemmed | Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data |
| title_short | Applying Machine Learning Techniques to the Analysis and Prediction of Financial Data |
| title_sort | applying machine learning techniques to the analysis and prediction of financial data |
| topic | Data analysis Prediction model Machine learning Financial industry Classification model |
| url | http://dspace.ucuenca.edu.ec/handle/123456789/44208 https://www.scopus.com/record/display.uri?eid=2-s2.0-85174680864&doi=10.1007%2f978-981-99-3091-3_69&origin=inward&txGid=084b8bc02db6d01de6ef029d5e1eccbf |
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